Khoros has filed a patent for a computer-implemented system that uses machine learning to optimize the scheduling of content items on social networks. The system receives content items associated with a user-selected topic, calculates a reaction profile based on user reactions to similar content, and determines an optimal posting schedule based on this profile. The patent also includes auto-scheduling capabilities that consider audience reaction and domain-specific schedule personalization. GlobalData’s report on Khoros gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Khoros, Personalized content delivery was a key innovation area identified from patents. Khoros's grant share as of September 2023 was 52%. Grant share is based on the ratio of number of grants to total number of patents.
The patent filed is for a method of scheduling content posting on social networks
A recently filed patent (Publication Number: US20230281663A1) describes a method and apparatus for optimizing the posting of content on social networks. The method involves receiving a request for a specific time interval to post content on multiple social networks. The interval is divided into smaller units of time, and transformation functions are created using machine learning to analyze data from one social network against another. The interval of time is then redefined with greater granularity, and a schedule for posting the content item is calculated based on this new interval.
The patent also introduces the concept of a decay function, which represents the level of interest of the audience associated with the social networks. The decay function is used to determine the optimal time to post the content item, and the schedule is recomputed based on this information. The content item is then automatically scheduled for posting based on the decay function, taking into account estimated data values representing audience reaction and domain-specific schedule personalization.
The apparatus described in the patent includes a processor and memory that execute instructions to perform the method. The processor receives the request for the time interval, creates transformation functions using machine learning, identifies the schedule and post-to-reaction filter, and redefines the interval with greater granularity. The processor also models the decay function, determines the optimal posting time, recomputes the schedule, and auto-schedules the posting of the content item based on the decay function.
Overall, this patent introduces a method and apparatus for optimizing the posting of content on social networks by using machine learning and a decay function to determine the optimal posting time. By analyzing data from different social networks and considering audience interest, this technology aims to increase reactions to published content and improve the effectiveness of social media campaigns.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
